| This Week’s Predictions | |||||
| Game | Prediction | Winner | Correct | Correct Votes | Correct Percent |
|---|---|---|---|---|---|
| 1 | Minnesota Vikings | Los Angeles Rams | No | 7 | 0.0551 |
| 2 | Tampa Bay Buccaneers | Atlanta Falcons | No | 61 | 0.4803 |
| 3 | Arizona Cardinals | Arizona Cardinals | Yes | 69 | 0.5433 |
| 4 | Baltimore Ravens | Cleveland Browns | No | 4 | 0.0315 |
| 5 | Green Bay Packers | Green Bay Packers | Yes | 122 | 0.9606 |
| 6 | Houston Texans | Houston Texans | Yes | 115 | 0.9055 |
| 7 | New York Jets | New England Patriots | No | 14 | 0.1102 |
| 8 | Philadelphia Eagles | Philadelphia Eagles | Yes | 76 | 0.5984 |
| 9 | Detroit Lions | Detroit Lions | Yes | 123 | 0.9685 |
| 10 | Los Angeles Chargers | Los Angeles Chargers | Yes | 100 | 0.7874 |
| 11 | Buffalo Bills | Buffalo Bills | Yes | 85 | 0.6693 |
| 12 | Denver Broncos | Denver Broncos | Yes | 124 | 0.9764 |
| 13 | Washington Commanders | Washington Commanders | Yes | 92 | 0.7244 |
| 14 | Kansas City Chiefs | Kansas City Chiefs | Yes | 121 | 0.9528 |
| 15 | San Francisco 49ers | San Francisco 49ers | Yes | 92 | 0.7244 |
| 16 | Pittsburgh Steelers | Pittsburgh Steelers | Yes | 120 | 0.9449 |
| Individual Results | |||||||||||||
| Week 8 | |||||||||||||
| Name | Weekly # Correct | Percent | Weeks Picked | Season Percent | Adj Season Percent | Season Trend | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Week 1 | Week 2 | Week 3 | Week 4 | Week 5 | Week 6 | Week 7 | Week 8 | ||||||
| Christopher Sims | 11 | 9 | 10 | 8 | 7 | 10 | 12 | 14 | 0.8750 | 8 | 0.6585 | 0.6585 | |
| Ryan Cvik | 10 | 8 | 9 | 11 | 9 | 11 | 11 | 13 | 0.8125 | 8 | 0.6667 | 0.6667 | |
| Robert Sokol | 10 | 8 | NA | NA | 6 | 9 | 9 | 13 | 0.8125 | 6 | 0.6044 | 0.4533 | |
| Thomas Brenstuhl | 9 | 8 | NA | 6 | 7 | 9 | 10 | 13 | 0.8125 | 7 | 0.5794 | 0.5070 | |
| Brian Patterson | 11 | 6 | 9 | 9 | 6 | NA | 9 | 13 | 0.8125 | 7 | 0.5780 | 0.5057 | |
| Edward Ford | 9 | 7 | 6 | 10 | 5 | 10 | 10 | 13 | 0.8125 | 8 | 0.5691 | 0.5691 | |
| Joshua Tracey | 12 | 5 | 8 | 6 | 7 | NA | 9 | 13 | 0.8125 | 7 | 0.5505 | 0.4817 | |
| Steven Maisonneuve | NA | NA | NA | NA | 11 | 10 | 11 | 12 | 0.7500 | 4 | 0.7458 | 0.3729 | |
| Matthew Blair | NA | NA | NA | NA | NA | 11 | 10 | 12 | 0.7500 | 3 | 0.7333 | 0.2750 | |
| Chris Papageorge | 14 | 8 | 10 | 11 | 8 | 12 | 12 | 12 | 0.7500 | 8 | 0.7073 | 0.7073 | |
| Heather Kohler | 12 | NA | 7 | 12 | 9 | 11 | NA | 12 | 0.7500 | 6 | 0.6848 | 0.5136 | |
| Randolph Tidd | 11 | 7 | 8 | 12 | NA | 12 | 11 | 12 | 0.7500 | 7 | 0.6697 | 0.5860 | |
| Nicholas Cinco | 12 | 8 | NA | NA | 6 | 11 | 11 | 12 | 0.7500 | 6 | 0.6593 | 0.4945 | |
| Anthony Brinson | 11 | 7 | NA | 9 | 10 | 11 | 9 | 12 | 0.7500 | 7 | 0.6449 | 0.5643 | |
| Matthew Schultz | 13 | 10 | 9 | 8 | 9 | 9 | 9 | 12 | 0.7500 | 8 | 0.6423 | 0.6423 | |
| Michelle Fraterrigo | 11 | 8 | 9 | 9 | 7 | 11 | 12 | 12 | 0.7500 | 8 | 0.6423 | 0.6423 | |
| Bryson Scott | 10 | 9 | 7 | NA | 7 | 12 | 11 | 12 | 0.7500 | 7 | 0.6355 | 0.5561 | |
| Patrick Tynan | 12 | 8 | 7 | 9 | 8 | 12 | NA | 12 | 0.7500 | 7 | 0.6296 | 0.5509 | |
| Nicole Dike | 13 | 7 | 8 | 10 | 7 | 10 | 10 | 12 | 0.7500 | 8 | 0.6260 | 0.6260 | |
| Keisha Vasquez | 8 | 7 | 9 | 9 | 11 | 11 | 9 | 12 | 0.7500 | 8 | 0.6179 | 0.6179 | |
| Daniel Baller | 14 | 6 | 9 | 8 | 7 | 9 | 10 | 12 | 0.7500 | 8 | 0.6098 | 0.6098 | |
| Nahir Shepard | 11 | 8 | 10 | 8 | 6 | 12 | 8 | 12 | 0.7500 | 8 | 0.6098 | 0.6098 | |
| George Brown | 14 | 7 | 8 | 7 | 6 | 11 | 10 | 12 | 0.7500 | 8 | 0.6098 | 0.6098 | |
| Vincent Scannelli | 11 | 7 | 7 | 11 | 8 | 8 | 11 | 12 | 0.7500 | 8 | 0.6098 | 0.6098 | |
| Pablo Burgosramos | 9 | 5 | 8 | 9 | 5 | 14 | 12 | 12 | 0.7500 | 8 | 0.6016 | 0.6016 | |
| Karen Richardson | 10 | 9 | 7 | 9 | 11 | 8 | 8 | 12 | 0.7500 | 8 | 0.6016 | 0.6016 | |
| Jennifer Arty | 10 | 7 | 9 | 7 | 7 | 12 | 8 | 12 | 0.7500 | 8 | 0.5854 | 0.5854 | |
| Kamar Morgan | 12 | 6 | 8 | 5 | 8 | 12 | 9 | 12 | 0.7500 | 8 | 0.5854 | 0.5854 | |
| Terry Hardison | 13 | 8 | 6 | 7 | 4 | 11 | 10 | 12 | 0.7500 | 8 | 0.5772 | 0.5772 | |
| Robert Lynch | 6 | 9 | 8 | 6 | 9 | 7 | 7 | 12 | 0.7500 | 8 | 0.5203 | 0.5203 | |
| Robert Cunningham | 14 | 9 | 10 | 12 | 8 | 12 | 11 | 11 | 0.6875 | 8 | 0.7073 | 0.7073 | |
| Robert Gelo | 14 | 8 | 9 | 9 | 8 | 13 | 13 | 11 | 0.6875 | 8 | 0.6911 | 0.6911 | |
| Kevin Kehoe | 13 | 7 | 9 | 10 | 8 | 13 | 12 | 11 | 0.6875 | 8 | 0.6748 | 0.6748 | |
| Anthony Bloss | 13 | 8 | 8 | 11 | 8 | 13 | 11 | 11 | 0.6875 | 8 | 0.6748 | 0.6748 | |
| Michael Linder | 11 | 9 | 9 | NA | NA | 12 | 10 | 11 | 0.6875 | 6 | 0.6667 | 0.5000 | |
| Travis Delagardelle | 11 | 12 | 10 | 8 | 6 | 11 | 12 | 11 | 0.6875 | 8 | 0.6585 | 0.6585 | |
| Nathan Brown | 13 | 8 | 9 | 11 | 9 | NA | 10 | 11 | 0.6875 | 7 | 0.6514 | 0.5700 | |
| David Dupree | 13 | 8 | 10 | 9 | 7 | 11 | 11 | 11 | 0.6875 | 8 | 0.6504 | 0.6504 | |
| George Sweet | 13 | 9 | 6 | 10 | 11 | 9 | 11 | 11 | 0.6875 | 8 | 0.6504 | 0.6504 | |
| Shawn Carden | 10 | 9 | 10 | 10 | 8 | 11 | 10 | 11 | 0.6875 | 8 | 0.6423 | 0.6423 | |
| Erik Neumann | 12 | 8 | 9 | 9 | 7 | 13 | 10 | 11 | 0.6875 | 8 | 0.6423 | 0.6423 | |
| Jeremy Stieler | 11 | 9 | 6 | 11 | 6 | 13 | 11 | 11 | 0.6875 | 8 | 0.6341 | 0.6341 | |
| Jennifer Bouland | 13 | 8 | 10 | 7 | 8 | 11 | 10 | 11 | 0.6875 | 8 | 0.6341 | 0.6341 | |
| Philip Driskill | 12 | 7 | 8 | 10 | 8 | NA | 13 | 11 | 0.6875 | 7 | 0.6330 | 0.5539 | |
| Brian Hollmann | NA | NA | NA | 8 | 8 | 10 | 10 | 11 | 0.6875 | 5 | 0.6267 | 0.3917 | |
| Heather Ellenberger | 13 | 8 | 7 | 8 | 7 | 12 | 11 | 11 | 0.6875 | 8 | 0.6260 | 0.6260 | |
| Matthew Olguin | 10 | 8 | 9 | 9 | 7 | 12 | 11 | 11 | 0.6875 | 8 | 0.6260 | 0.6260 | |
| Antonio Mitchell | 11 | 7 | 8 | 9 | 9 | 11 | 10 | 11 | 0.6875 | 8 | 0.6179 | 0.6179 | |
| Daniel Halse | 12 | 6 | 8 | 10 | 7 | 13 | 9 | 11 | 0.6875 | 8 | 0.6179 | 0.6179 | |
| Jeffrey Rudderforth | 11 | 11 | 10 | 9 | 6 | 7 | 10 | 11 | 0.6875 | 8 | 0.6098 | 0.6098 | |
| Diance Durand | 9 | 9 | 12 | 7 | 8 | 10 | 9 | 11 | 0.6875 | 8 | 0.6098 | 0.6098 | |
| Jared Kaanga | 11 | 9 | 9 | 8 | 7 | 10 | 9 | 11 | 0.6875 | 8 | 0.6016 | 0.6016 | |
| Michael Branson | 9 | 8 | 8 | 9 | 8 | 11 | 9 | 11 | 0.6875 | 8 | 0.5935 | 0.5935 | |
| Walter Archambo | 8 | 8 | 7 | 9 | 6 | 12 | 11 | 11 | 0.6875 | 8 | 0.5854 | 0.5854 | |
| Rachel Follo | 15 | 8 | 6 | 6 | 9 | 7 | 10 | 11 | 0.6875 | 8 | 0.5854 | 0.5854 | |
| Noah Gosswiller | 8 | 7 | NA | 10 | 8 | NA | 10 | 11 | 0.6875 | 6 | 0.5806 | 0.4354 | |
| Jeffrey Zornes | 9 | 11 | 6 | 8 | 7 | 10 | 9 | 11 | 0.6875 | 8 | 0.5772 | 0.5772 | |
| Zechariah Ziebarth | 8 | 8 | 8 | 10 | 5 | 10 | 10 | 11 | 0.6875 | 8 | 0.5691 | 0.5691 | |
| Louie Renew | 9 | 8 | 12 | 4 | 10 | 8 | 8 | 11 | 0.6875 | 8 | 0.5691 | 0.5691 | |
| Jonathan Smith | 11 | NA | 4 | 10 | 7 | NA | 8 | 11 | 0.6875 | 6 | 0.5484 | 0.4113 | |
| Jason Jackson | 12 | 7 | 5 | 6 | 5 | 12 | 9 | 11 | 0.6875 | 8 | 0.5447 | 0.5447 | |
| Richard Conkle | 7 | 6 | 6 | 8 | 7 | 10 | 12 | 11 | 0.6875 | 8 | 0.5447 | 0.5447 | |
| Steven Webster | 7 | 7 | 9 | 6 | 7 | 9 | NA | 11 | 0.6875 | 7 | 0.5185 | 0.4537 | |
| Wayne Schofield | 7 | 5 | 9 | 5 | 7 | 7 | 11 | 11 | 0.6875 | 8 | 0.5041 | 0.5041 | |
| Jeremy Krammes | 12 | NA | NA | NA | NA | NA | NA | 10 | 0.6250 | 2 | 0.6875 | 0.1719 | |
| Bruce Williams | 13 | 9 | 10 | 8 | 9 | 13 | 12 | 10 | 0.6250 | 8 | 0.6829 | 0.6829 | |
| Chester Todd | 13 | 8 | 8 | 8 | 9 | 13 | 13 | 10 | 0.6250 | 8 | 0.6667 | 0.6667 | |
| Michael Pacifico | 13 | 8 | 7 | 9 | 9 | 12 | 12 | 10 | 0.6250 | 8 | 0.6504 | 0.6504 | |
| Bradley Hobson | 13 | 7 | 8 | 11 | 7 | 13 | 10 | 10 | 0.6250 | 8 | 0.6423 | 0.6423 | |
| Jordan Forwood | 11 | 8 | 6 | 11 | NA | 13 | NA | 10 | 0.6250 | 6 | 0.6277 | 0.4708 | |
| Paul Presti | 12 | 8 | 9 | 12 | 7 | 11 | 8 | 10 | 0.6250 | 8 | 0.6260 | 0.6260 | |
| Randy Dick | 11 | 7 | 8 | 8 | 9 | 14 | 10 | 10 | 0.6250 | 8 | 0.6260 | 0.6260 | |
| Rafael Torres | 12 | 9 | 8 | 7 | 8 | 10 | 12 | 10 | 0.6250 | 8 | 0.6179 | 0.6179 | |
| Richard Beeghley | 11 | 7 | 6 | 11 | 7 | 14 | 10 | 10 | 0.6250 | 8 | 0.6179 | 0.6179 | |
| Ramar Williams | 10 | 8 | 7 | 11 | 8 | 11 | 11 | 10 | 0.6250 | 8 | 0.6179 | 0.6179 | |
| Kenneth Nielsen | 13 | 8 | 7 | NA | 8 | 9 | 11 | 10 | 0.6250 | 7 | 0.6168 | 0.5397 | |
| Brayant Rivera | 10 | 8 | 9 | 8 | 6 | 13 | 11 | 10 | 0.6250 | 8 | 0.6098 | 0.6098 | |
| Jonathon Leslein | 10 | 8 | 7 | 10 | 8 | 12 | 10 | 10 | 0.6250 | 8 | 0.6098 | 0.6098 | |
| Darryle Sellers | 11 | 11 | 6 | 8 | 9 | 11 | 9 | 10 | 0.6250 | 8 | 0.6098 | 0.6098 | |
| Jason Schattel | 13 | 7 | 6 | 9 | 10 | 11 | 9 | 10 | 0.6250 | 8 | 0.6098 | 0.6098 | |
| Ryan Baum | 14 | 4 | 9 | 10 | 9 | NA | 10 | 10 | 0.6250 | 7 | 0.6055 | 0.5298 | |
| Montee Brown | 10 | 6 | 8 | 7 | 8 | 14 | 11 | 10 | 0.6250 | 8 | 0.6016 | 0.6016 | |
| Daniel Major | 8 | 10 | 11 | 6 | 8 | 11 | NA | 10 | 0.6250 | 7 | 0.5926 | 0.5185 | |
| Megan Fitzgerald | 8 | 11 | 9 | 10 | NA | NA | 8 | 10 | 0.6250 | 6 | 0.5895 | 0.4421 | |
| Earl Dixon | 10 | 9 | 6 | 9 | 9 | NA | 11 | 10 | 0.6250 | 7 | 0.5872 | 0.5138 | |
| Melissa Printup | 8 | 9 | 9 | 6 | 10 | 10 | 10 | 10 | 0.6250 | 8 | 0.5854 | 0.5854 | |
| Scott Lefton | 10 | 8 | 8 | 7 | 7 | 11 | 11 | 10 | 0.6250 | 8 | 0.5854 | 0.5854 | |
| Thomas Mccoy | 10 | 7 | 6 | 8 | 9 | 11 | 11 | 10 | 0.6250 | 8 | 0.5854 | 0.5854 | |
| Steward Hogans | 10 | 7 | 10 | NA | NA | NA | NA | 10 | 0.6250 | 4 | 0.5781 | 0.2890 | |
| Tara Bridgett | 11 | 8 | 8 | 8 | NA | 9 | NA | 10 | 0.6250 | 6 | 0.5745 | 0.4309 | |
| Ronald Schmidt | 10 | 10 | 5 | 9 | 6 | 8 | 12 | 10 | 0.6250 | 8 | 0.5691 | 0.5691 | |
| Min Choi | 10 | NA | 7 | NA | 8 | 7 | NA | 10 | 0.6250 | 5 | 0.5526 | 0.3454 | |
| Sheryl Claiborne-Smith | 11 | 7 | NA | NA | NA | 7 | 7 | 10 | 0.6250 | 5 | 0.5455 | 0.3409 | |
| Kyle May | 10 | 8 | 5 | 6 | 8 | NA | 12 | 10 | 0.6250 | 7 | 0.5413 | 0.4736 | |
| Gary Lawrence | 10 | 6 | 5 | 5 | 7 | 9 | 9 | 10 | 0.6250 | 8 | 0.4959 | 0.4959 | |
| Keven Talbert | 10 | 7 | 9 | 11 | 9 | 14 | 13 | 9 | 0.5625 | 8 | 0.6667 | 0.6667 | |
| Marc Agne | 14 | 7 | 9 | 13 | 6 | 13 | 10 | 9 | 0.5625 | 8 | 0.6585 | 0.6585 | |
| Aubrey Conn | 13 | 7 | 10 | 9 | 8 | 12 | 12 | 9 | 0.5625 | 8 | 0.6504 | 0.6504 | |
| William Schouviller | 12 | 7 | 9 | 9 | 11 | 13 | 10 | 9 | 0.5625 | 8 | 0.6504 | 0.6504 | |
| Karen Coleman | 13 | 6 | NA | 11 | 9 | 9 | 10 | 9 | 0.5625 | 7 | 0.6262 | 0.5479 | |
| James Small | 12 | NA | 9 | 10 | 8 | 10 | 9 | 9 | 0.5625 | 7 | 0.6262 | 0.5479 | |
| Michael Moss | 13 | 8 | 8 | 8 | 10 | 13 | 8 | 9 | 0.5625 | 8 | 0.6260 | 0.6260 | |
| Clevante Granville | 9 | 11 | NA | NA | 5 | 11 | 11 | 9 | 0.5625 | 6 | 0.6154 | 0.4615 | |
| Kevin Buettner | 12 | 8 | 8 | 10 | 7 | 11 | 10 | 9 | 0.5625 | 8 | 0.6098 | 0.6098 | |
| Gregory Brown | 15 | 7 | 6 | 9 | 8 | 12 | 9 | 9 | 0.5625 | 8 | 0.6098 | 0.6098 | |
| Derrick Elam | 13 | 9 | 8 | 11 | 7 | 10 | 8 | 9 | 0.5625 | 8 | 0.6098 | 0.6098 | |
| Stephen Bush | 9 | 7 | 4 | 10 | 9 | 13 | 13 | 9 | 0.5625 | 8 | 0.6016 | 0.6016 | |
| Nicholas Nguyen | 11 | 8 | 5 | 8 | 7 | 12 | 11 | 9 | 0.5625 | 8 | 0.5772 | 0.5772 | |
| Yiming Hu | 12 | NA | 7 | 7 | 6 | 8 | 12 | 9 | 0.5625 | 7 | 0.5701 | 0.4988 | |
| Cade Martinez | 10 | 7 | 8 | 8 | 6 | 11 | 11 | 9 | 0.5625 | 8 | 0.5691 | 0.5691 | |
| Cheryl Brown | 11 | 6 | 9 | 8 | 8 | 10 | NA | 9 | 0.5625 | 7 | 0.5648 | 0.4942 | |
| Marcus Evans | 11 | 8 | NA | 8 | 7 | 10 | 7 | 9 | 0.5625 | 7 | 0.5607 | 0.4906 | |
| Christopher Mulcahy | 11 | 9 | 7 | 8 | NA | 8 | 9 | 9 | 0.5625 | 7 | 0.5596 | 0.4896 | |
| Jose Torres Mendoza | 12 | 8 | 8 | 8 | NA | NA | 8 | 9 | 0.5625 | 6 | 0.5579 | 0.4184 | |
| Michael Moore | 11 | 6 | 7 | 7 | 8 | 12 | NA | 9 | 0.5625 | 7 | 0.5556 | 0.4861 | |
| Bunnaro Sun | 12 | 5 | 8 | 11 | 6 | 8 | 9 | 9 | 0.5625 | 8 | 0.5528 | 0.5528 | |
| Jack Wheeler | 9 | 6 | 5 | 10 | 8 | NA | 9 | 9 | 0.5625 | 7 | 0.5138 | 0.4496 | |
| Andrew Gray | 5 | 8 | 9 | 7 | NA | NA | 7 | 9 | 0.5625 | 6 | 0.4737 | 0.3553 | |
| Shaun Dahl | 14 | 7 | 9 | 11 | 10 | 10 | 10 | 8 | 0.5000 | 8 | 0.6423 | 0.6423 | |
| Kevin Green | 11 | 9 | NA | 8 | 7 | 12 | NA | 8 | 0.5000 | 6 | 0.5978 | 0.4484 | |
| Kristen White | 14 | 7 | 9 | 9 | 8 | 9 | 9 | 8 | 0.5000 | 8 | 0.5935 | 0.5935 | |
| Thomas Cho | 10 | 6 | NA | 11 | 7 | 12 | NA | 8 | 0.5000 | 6 | 0.5870 | 0.4402 | |
| David Humes | 10 | 9 | 8 | 11 | 5 | 8 | 12 | 8 | 0.5000 | 8 | 0.5772 | 0.5772 | |
| Trevor Macgavin | 12 | 7 | 10 | 8 | 8 | 8 | 9 | 7 | 0.4375 | 8 | 0.5610 | 0.5610 | |
| Gabriel Quinones | 10 | 7 | 6 | 9 | NA | 11 | 8 | 7 | 0.4375 | 7 | 0.5321 | 0.4656 | |
| Akilah Gamble | 9 | NA | 12 | 9 | 6 | 8 | 12 | 6 | 0.3750 | 7 | 0.5794 | 0.5070 | |
| Ashlyn Dortch | 9 | NA | NA | 8 | NA | 5 | 9 | 6 | 0.3750 | 5 | 0.4805 | 0.3003 | |
| Clayton Grimes | 14 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.8750 | 0.1094 | |
| Tanaysa Henderson | NA | NA | NA | NA | NA | 12 | NA | NA | 0.0000 | 1 | 0.8571 | 0.1071 | |
| Brittany Pillar | NA | NA | NA | NA | NA | 10 | 12 | NA | 0.0000 | 2 | 0.7586 | 0.1897 | |
| Wallace Savage | 12 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.7500 | 0.0938 | |
| Brian Holder | 12 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.7500 | 0.0938 | |
| Sandra Carter | 12 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.7500 | 0.0938 | |
| Terrence Lee | 11 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.6875 | 0.0859 | |
| Daniel Gray | 11 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.6875 | 0.0859 | |
| Jeremy Mounce | 12 | 8 | 8 | NA | 10 | 12 | NA | NA | 0.0000 | 5 | 0.6579 | 0.4112 | |
| Pamela Augustine | 14 | 9 | 9 | NA | 7 | 11 | 9 | NA | 0.0000 | 6 | 0.6484 | 0.4863 | |
| Paul Seitz | 11 | 9 | 9 | NA | 8 | 10 | 11 | NA | 0.0000 | 6 | 0.6374 | 0.4780 | |
| George Hall | 12 | NA | 8 | NA | NA | NA | NA | NA | 0.0000 | 2 | 0.6250 | 0.1562 | |
| Wayne Gokey | 13 | 7 | NA | 11 | NA | NA | 8 | NA | 0.0000 | 4 | 0.6190 | 0.3095 | |
| David Hadley | 13 | 10 | 8 | NA | 8 | NA | 8 | NA | 0.0000 | 5 | 0.6104 | 0.3815 | |
| Amy Asberry | 11 | 8 | 6 | 10 | NA | 12 | 9 | NA | 0.0000 | 6 | 0.6022 | 0.4516 | |
| Jonathan Knight | 13 | 10 | 9 | 6 | 7 | NA | 11 | NA | 0.0000 | 6 | 0.6022 | 0.4516 | |
| Brandon Parks | 12 | 6 | 9 | 9 | 6 | 13 | NA | NA | 0.0000 | 6 | 0.5978 | 0.4484 | |
| Jeffrey Dusza | 11 | 8 | NA | NA | NA | NA | NA | NA | 0.0000 | 2 | 0.5938 | 0.1484 | |
| Cherylynn Vidal | 13 | 9 | 8 | 8 | NA | NA | NA | NA | 0.0000 | 4 | 0.5938 | 0.2969 | |
| Adam Konkle | 10 | 9 | NA | NA | NA | NA | NA | NA | 0.0000 | 2 | 0.5938 | 0.1484 | |
| Jason Miranda | 10 | 7 | 8 | NA | 9 | 11 | 8 | NA | 0.0000 | 6 | 0.5824 | 0.4368 | |
| Jennifer Wilson | 11 | 9 | 10 | 6 | NA | 7 | 11 | NA | 0.0000 | 6 | 0.5806 | 0.4354 | |
| Darvin Graham | 12 | 7 | 6 | 9 | 8 | 11 | 9 | NA | 0.0000 | 7 | 0.5794 | 0.5070 | |
| Ryan Shipley | 11 | 6 | 10 | 8 | 5 | 9 | 11 | NA | 0.0000 | 7 | 0.5607 | 0.4906 | |
| Joseph Martin | 10 | 7 | 8 | 8 | 8 | 10 | 9 | NA | 0.0000 | 7 | 0.5607 | 0.4906 | |
| George Mancini | 11 | 8 | 6 | NA | 8 | 6 | 12 | NA | 0.0000 | 6 | 0.5604 | 0.4203 | |
| Desmond Jenkins | 10 | 7 | 7 | NA | 7 | 12 | 8 | NA | 0.0000 | 6 | 0.5604 | 0.4203 | |
| David Plate | 10 | 8 | 8 | 8 | 9 | NA | NA | NA | 0.0000 | 5 | 0.5513 | 0.3446 | |
| Lawrence Thuotte | 9 | 5 | 12 | NA | 8 | NA | NA | NA | 0.0000 | 4 | 0.5484 | 0.2742 | |
| Donald Park | 9 | NA | 6 | NA | NA | 10 | NA | NA | 0.0000 | 3 | 0.5435 | 0.2038 | |
| Anthony Rockemore | 13 | 8 | 6 | 8 | 7 | NA | 8 | NA | 0.0000 | 6 | 0.5376 | 0.4032 | |
| Monte Henderson | 9 | 8 | NA | NA | NA | NA | NA | NA | 0.0000 | 2 | 0.5312 | 0.1328 | |
| David Kim | 9 | 8 | NA | NA | NA | NA | NA | NA | 0.0000 | 2 | 0.5312 | 0.1328 | |
| Jamie Ainsleigh-Wong | 9 | 8 | 9 | 9 | 8 | 5 | NA | NA | 0.0000 | 6 | 0.5217 | 0.3913 | |
| Robert Martin | 7 | NA | 9 | 8 | 8 | 8 | 7 | NA | 0.0000 | 6 | 0.5165 | 0.3874 | |
| Jay Kelly | 10 | 9 | 7 | 7 | 5 | 10 | 7 | NA | 0.0000 | 7 | 0.5140 | 0.4498 | |
| Zachary Brosemer | 8 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.5000 | 0.0625 | |
| Antonio Chapa | 8 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.5000 | 0.0625 | |
| Vincent Kandian | 9 | 8 | 8 | 7 | NA | NA | NA | NA | 0.0000 | 4 | 0.5000 | 0.2500 | |
| Gabrieal Feiling | 10 | NA | 5 | NA | NA | NA | NA | NA | 0.0000 | 2 | 0.4688 | 0.1172 | |
| Ashley Johnson | 9 | NA | 6 | NA | 6 | NA | NA | NA | 0.0000 | 3 | 0.4565 | 0.1712 | |
| Jasprin Smith | 6 | NA | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.3750 | 0.0469 | |
| Robert Epps | NA | 6 | NA | NA | NA | NA | NA | NA | 0.0000 | 1 | 0.3750 | 0.0469 | |
| Season Leaderboard (Season Percent) | ||||||
| Week 8 | ||||||
| Season Rank | Name | Donuts Won | Weeks Picked | Season Percent | Adj Season Percent | Season Trend |
|---|---|---|---|---|---|---|
| 1 | Clayton Grimes | 0 | 1 | 0.8750 | 0.1094 | |
| 2 | Tanaysa Henderson | 0 | 1 | 0.8571 | 0.1071 | |
| 3 | Brittany Pillar | 0 | 2 | 0.7586 | 0.1897 | |
| 4 | Brian Holder | 0 | 1 | 0.7500 | 0.0938 | |
| 4 | Sandra Carter | 0 | 1 | 0.7500 | 0.0938 | |
| 4 | Wallace Savage | 0 | 1 | 0.7500 | 0.0938 | |
| 7 | Steven Maisonneuve | 1 | 4 | 0.7458 | 0.3729 | |
| 8 | Matthew Blair | 0 | 3 | 0.7333 | 0.2750 | |
| 9 | Chris Papageorge | 0 | 8 | 0.7073 | 0.7073 | |
| 9 | Robert Cunningham | 0 | 8 | 0.7073 | 0.7073 | |
| 11 | Robert Gelo | 1 | 8 | 0.6911 | 0.6911 | |
| 12 | Daniel Gray | 0 | 1 | 0.6875 | 0.0859 | |
| 12 | Jeremy Krammes | 0 | 2 | 0.6875 | 0.1719 | |
| 12 | Terrence Lee | 0 | 1 | 0.6875 | 0.0859 | |
| 15 | Heather Kohler | 0 | 6 | 0.6848 | 0.5136 | |
| 16 | Bruce Williams | 0 | 8 | 0.6829 | 0.6829 | |
| 17 | Anthony Bloss | 0 | 8 | 0.6748 | 0.6748 | |
| 17 | Kevin Kehoe | 0 | 8 | 0.6748 | 0.6748 | |
| 19 | Randolph Tidd | 0 | 7 | 0.6697 | 0.5860 | |
| 20 | Chester Todd | 1 | 8 | 0.6667 | 0.6667 | |
| 20 | Keven Talbert | 2 | 8 | 0.6667 | 0.6667 | |
| 20 | Michael Linder | 0 | 6 | 0.6667 | 0.5000 | |
| 20 | Ryan Cvik | 0 | 8 | 0.6667 | 0.6667 | |
| 24 | Nicholas Cinco | 0 | 6 | 0.6593 | 0.4945 | |
| 25 | Christopher Sims | 1 | 8 | 0.6585 | 0.6585 | |
| 25 | Marc Agne | 1 | 8 | 0.6585 | 0.6585 | |
| 25 | Travis Delagardelle | 1 | 8 | 0.6585 | 0.6585 | |
| 28 | Jeremy Mounce | 0 | 5 | 0.6579 | 0.4112 | |
| 29 | Nathan Brown | 0 | 7 | 0.6514 | 0.5700 | |
| 30 | Aubrey Conn | 0 | 8 | 0.6504 | 0.6504 | |
| 30 | David Dupree | 0 | 8 | 0.6504 | 0.6504 | |
| 30 | George Sweet | 1 | 8 | 0.6504 | 0.6504 | |
| 30 | Michael Pacifico | 0 | 8 | 0.6504 | 0.6504 | |
| 30 | William Schouviller | 1 | 8 | 0.6504 | 0.6504 | |
| 35 | Pamela Augustine | 0 | 6 | 0.6484 | 0.4863 | |
| 36 | Anthony Brinson | 0 | 7 | 0.6449 | 0.5643 | |
| 37 | Bradley Hobson | 0 | 8 | 0.6423 | 0.6423 | |
| 37 | Erik Neumann | 0 | 8 | 0.6423 | 0.6423 | |
| 37 | Matthew Schultz | 0 | 8 | 0.6423 | 0.6423 | |
| 37 | Michelle Fraterrigo | 0 | 8 | 0.6423 | 0.6423 | |
| 37 | Shaun Dahl | 0 | 8 | 0.6423 | 0.6423 | |
| 37 | Shawn Carden | 0 | 8 | 0.6423 | 0.6423 | |
| 43 | Paul Seitz | 0 | 6 | 0.6374 | 0.4780 | |
| 44 | Bryson Scott | 0 | 7 | 0.6355 | 0.5561 | |
| 45 | Jennifer Bouland | 0 | 8 | 0.6341 | 0.6341 | |
| 45 | Jeremy Stieler | 0 | 8 | 0.6341 | 0.6341 | |
| 47 | Philip Driskill | 1 | 7 | 0.6330 | 0.5539 | |
| 48 | Patrick Tynan | 0 | 7 | 0.6296 | 0.5509 | |
| 49 | Jordan Forwood | 0 | 6 | 0.6277 | 0.4708 | |
| 50 | Brian Hollmann | 0 | 5 | 0.6267 | 0.3917 | |
| 51 | James Small | 0 | 7 | 0.6262 | 0.5479 | |
| 51 | Karen Coleman | 0 | 7 | 0.6262 | 0.5479 | |
| 53 | Heather Ellenberger | 0 | 8 | 0.6260 | 0.6260 | |
| 53 | Matthew Olguin | 0 | 8 | 0.6260 | 0.6260 | |
| 53 | Michael Moss | 0 | 8 | 0.6260 | 0.6260 | |
| 53 | Nicole Dike | 0 | 8 | 0.6260 | 0.6260 | |
| 53 | Paul Presti | 0 | 8 | 0.6260 | 0.6260 | |
| 53 | Randy Dick | 1 | 8 | 0.6260 | 0.6260 | |
| 59 | George Hall | 0 | 2 | 0.6250 | 0.1562 | |
| 60 | Wayne Gokey | 0 | 4 | 0.6190 | 0.3095 | |
| 61 | Antonio Mitchell | 0 | 8 | 0.6179 | 0.6179 | |
| 61 | Daniel Halse | 0 | 8 | 0.6179 | 0.6179 | |
| 61 | Keisha Vasquez | 1 | 8 | 0.6179 | 0.6179 | |
| 61 | Rafael Torres | 0 | 8 | 0.6179 | 0.6179 | |
| 61 | Ramar Williams | 0 | 8 | 0.6179 | 0.6179 | |
| 61 | Richard Beeghley | 1 | 8 | 0.6179 | 0.6179 | |
| 67 | Kenneth Nielsen | 0 | 7 | 0.6168 | 0.5397 | |
| 68 | Clevante Granville | 0 | 6 | 0.6154 | 0.4615 | |
| 69 | David Hadley | 0 | 5 | 0.6104 | 0.3815 | |
| 70 | Brayant Rivera | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Daniel Baller | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Darryle Sellers | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Derrick Elam | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Diance Durand | 1 | 8 | 0.6098 | 0.6098 | |
| 70 | George Brown | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Gregory Brown | 1 | 8 | 0.6098 | 0.6098 | |
| 70 | Jason Schattel | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Jeffrey Rudderforth | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Jonathon Leslein | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Kevin Buettner | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Nahir Shepard | 0 | 8 | 0.6098 | 0.6098 | |
| 70 | Vincent Scannelli | 0 | 8 | 0.6098 | 0.6098 | |
| 83 | Ryan Baum | 0 | 7 | 0.6055 | 0.5298 | |
| 84 | Robert Sokol | 0 | 6 | 0.6044 | 0.4533 | |
| 85 | Amy Asberry | 0 | 6 | 0.6022 | 0.4516 | |
| 85 | Jonathan Knight | 0 | 6 | 0.6022 | 0.4516 | |
| 87 | Jared Kaanga | 0 | 8 | 0.6016 | 0.6016 | |
| 87 | Karen Richardson | 1 | 8 | 0.6016 | 0.6016 | |
| 87 | Montee Brown | 1 | 8 | 0.6016 | 0.6016 | |
| 87 | Pablo Burgosramos | 1 | 8 | 0.6016 | 0.6016 | |
| 87 | Stephen Bush | 1 | 8 | 0.6016 | 0.6016 | |
| 92 | Brandon Parks | 0 | 6 | 0.5978 | 0.4484 | |
| 92 | Kevin Green | 0 | 6 | 0.5978 | 0.4484 | |
| 94 | Adam Konkle | 0 | 2 | 0.5938 | 0.1484 | |
| 94 | Cherylynn Vidal | 0 | 4 | 0.5938 | 0.2969 | |
| 94 | Jeffrey Dusza | 0 | 2 | 0.5938 | 0.1484 | |
| 97 | Kristen White | 0 | 8 | 0.5935 | 0.5935 | |
| 97 | Michael Branson | 0 | 8 | 0.5935 | 0.5935 | |
| 99 | Daniel Major | 0 | 7 | 0.5926 | 0.5185 | |
| 100 | Megan Fitzgerald | 0 | 6 | 0.5895 | 0.4421 | |
| 101 | Earl Dixon | 0 | 7 | 0.5872 | 0.5138 | |
| 102 | Thomas Cho | 0 | 6 | 0.5870 | 0.4402 | |
| 103 | Jennifer Arty | 0 | 8 | 0.5854 | 0.5854 | |
| 103 | Kamar Morgan | 0 | 8 | 0.5854 | 0.5854 | |
| 103 | Melissa Printup | 0 | 8 | 0.5854 | 0.5854 | |
| 103 | Rachel Follo | 1 | 8 | 0.5854 | 0.5854 | |
| 103 | Scott Lefton | 0 | 8 | 0.5854 | 0.5854 | |
| 103 | Thomas Mccoy | 0 | 8 | 0.5854 | 0.5854 | |
| 103 | Walter Archambo | 0 | 8 | 0.5854 | 0.5854 | |
| 110 | Jason Miranda | 0 | 6 | 0.5824 | 0.4368 | |
| 111 | Jennifer Wilson | 0 | 6 | 0.5806 | 0.4354 | |
| 111 | Noah Gosswiller | 0 | 6 | 0.5806 | 0.4354 | |
| 113 | Akilah Gamble | 1 | 7 | 0.5794 | 0.5070 | |
| 113 | Darvin Graham | 0 | 7 | 0.5794 | 0.5070 | |
| 113 | Thomas Brenstuhl | 0 | 7 | 0.5794 | 0.5070 | |
| 116 | Steward Hogans | 0 | 4 | 0.5781 | 0.2890 | |
| 117 | Brian Patterson | 0 | 7 | 0.5780 | 0.5057 | |
| 118 | David Humes | 0 | 8 | 0.5772 | 0.5772 | |
| 118 | Jeffrey Zornes | 0 | 8 | 0.5772 | 0.5772 | |
| 118 | Nicholas Nguyen | 0 | 8 | 0.5772 | 0.5772 | |
| 118 | Terry Hardison | 0 | 8 | 0.5772 | 0.5772 | |
| 122 | Tara Bridgett | 0 | 6 | 0.5745 | 0.4309 | |
| 123 | Yiming Hu | 0 | 7 | 0.5701 | 0.4988 | |
| 124 | Cade Martinez | 0 | 8 | 0.5691 | 0.5691 | |
| 124 | Edward Ford | 0 | 8 | 0.5691 | 0.5691 | |
| 124 | Louie Renew | 1 | 8 | 0.5691 | 0.5691 | |
| 124 | Ronald Schmidt | 0 | 8 | 0.5691 | 0.5691 | |
| 124 | Zechariah Ziebarth | 0 | 8 | 0.5691 | 0.5691 | |
| 129 | Cheryl Brown | 0 | 7 | 0.5648 | 0.4942 | |
| 130 | Trevor Macgavin | 0 | 8 | 0.5610 | 0.5610 | |
| 131 | Joseph Martin | 0 | 7 | 0.5607 | 0.4906 | |
| 131 | Marcus Evans | 0 | 7 | 0.5607 | 0.4906 | |
| 131 | Ryan Shipley | 0 | 7 | 0.5607 | 0.4906 | |
| 134 | Desmond Jenkins | 0 | 6 | 0.5604 | 0.4203 | |
| 134 | George Mancini | 0 | 6 | 0.5604 | 0.4203 | |
| 136 | Christopher Mulcahy | 0 | 7 | 0.5596 | 0.4896 | |
| 137 | Jose Torres Mendoza | 0 | 6 | 0.5579 | 0.4184 | |
| 138 | Michael Moore | 0 | 7 | 0.5556 | 0.4861 | |
| 139 | Bunnaro Sun | 0 | 8 | 0.5528 | 0.5528 | |
| 140 | Min Choi | 0 | 5 | 0.5526 | 0.3454 | |
| 141 | David Plate | 0 | 5 | 0.5513 | 0.3446 | |
| 142 | Joshua Tracey | 0 | 7 | 0.5505 | 0.4817 | |
| 143 | Jonathan Smith | 0 | 6 | 0.5484 | 0.4113 | |
| 143 | Lawrence Thuotte | 1 | 4 | 0.5484 | 0.2742 | |
| 145 | Sheryl Claiborne-Smith | 0 | 5 | 0.5455 | 0.3409 | |
| 146 | Jason Jackson | 0 | 8 | 0.5447 | 0.5447 | |
| 146 | Richard Conkle | 0 | 8 | 0.5447 | 0.5447 | |
| 148 | Donald Park | 0 | 3 | 0.5435 | 0.2038 | |
| 149 | Kyle May | 0 | 7 | 0.5413 | 0.4736 | |
| 150 | Anthony Rockemore | 0 | 6 | 0.5376 | 0.4032 | |
| 151 | Gabriel Quinones | 0 | 7 | 0.5321 | 0.4656 | |
| 152 | David Kim | 0 | 2 | 0.5312 | 0.1328 | |
| 152 | Monte Henderson | 0 | 2 | 0.5312 | 0.1328 | |
| 154 | Jamie Ainsleigh-Wong | 0 | 6 | 0.5217 | 0.3913 | |
| 155 | Robert Lynch | 0 | 8 | 0.5203 | 0.5203 | |
| 156 | Steven Webster | 0 | 7 | 0.5185 | 0.4537 | |
| 157 | Robert Martin | 0 | 6 | 0.5165 | 0.3874 | |
| 158 | Jay Kelly | 0 | 7 | 0.5140 | 0.4498 | |
| 159 | Jack Wheeler | 0 | 7 | 0.5138 | 0.4496 | |
| 160 | Wayne Schofield | 0 | 8 | 0.5041 | 0.5041 | |
| 161 | Antonio Chapa | 0 | 1 | 0.5000 | 0.0625 | |
| 161 | Vincent Kandian | 0 | 4 | 0.5000 | 0.2500 | |
| 161 | Zachary Brosemer | 0 | 1 | 0.5000 | 0.0625 | |
| 164 | Gary Lawrence | 0 | 8 | 0.4959 | 0.4959 | |
| 165 | Ashlyn Dortch | 0 | 5 | 0.4805 | 0.3003 | |
| 166 | Andrew Gray | 0 | 6 | 0.4737 | 0.3553 | |
| 167 | Gabrieal Feiling | 0 | 2 | 0.4688 | 0.1172 | |
| 168 | Ashley Johnson | 0 | 3 | 0.4565 | 0.1712 | |
| 169 | Jasprin Smith | 0 | 1 | 0.3750 | 0.0469 | |
| 169 | Robert Epps | 0 | 1 | 0.3750 | 0.0469 | |
| Season Leaderboard (Adjusted Season Percent) | ||||||
| Week 8 | ||||||
| Season Rank | Name | Donuts Won | Weeks Picked | Season Percent | Adj Season Percent | Season Trend |
|---|---|---|---|---|---|---|
| 1 | Chris Papageorge | 0 | 8 | 0.7073 | 0.7073 | |
| 1 | Robert Cunningham | 0 | 8 | 0.7073 | 0.7073 | |
| 3 | Robert Gelo | 1 | 8 | 0.6911 | 0.6911 | |
| 4 | Bruce Williams | 0 | 8 | 0.6829 | 0.6829 | |
| 5 | Anthony Bloss | 0 | 8 | 0.6748 | 0.6748 | |
| 5 | Kevin Kehoe | 0 | 8 | 0.6748 | 0.6748 | |
| 7 | Chester Todd | 1 | 8 | 0.6667 | 0.6667 | |
| 7 | Keven Talbert | 2 | 8 | 0.6667 | 0.6667 | |
| 7 | Ryan Cvik | 0 | 8 | 0.6667 | 0.6667 | |
| 10 | Christopher Sims | 1 | 8 | 0.6585 | 0.6585 | |
| 10 | Marc Agne | 1 | 8 | 0.6585 | 0.6585 | |
| 10 | Travis Delagardelle | 1 | 8 | 0.6585 | 0.6585 | |
| 13 | Aubrey Conn | 0 | 8 | 0.6504 | 0.6504 | |
| 13 | David Dupree | 0 | 8 | 0.6504 | 0.6504 | |
| 13 | George Sweet | 1 | 8 | 0.6504 | 0.6504 | |
| 13 | Michael Pacifico | 0 | 8 | 0.6504 | 0.6504 | |
| 13 | William Schouviller | 1 | 8 | 0.6504 | 0.6504 | |
| 18 | Bradley Hobson | 0 | 8 | 0.6423 | 0.6423 | |
| 18 | Erik Neumann | 0 | 8 | 0.6423 | 0.6423 | |
| 18 | Matthew Schultz | 0 | 8 | 0.6423 | 0.6423 | |
| 18 | Michelle Fraterrigo | 0 | 8 | 0.6423 | 0.6423 | |
| 18 | Shaun Dahl | 0 | 8 | 0.6423 | 0.6423 | |
| 18 | Shawn Carden | 0 | 8 | 0.6423 | 0.6423 | |
| 24 | Jennifer Bouland | 0 | 8 | 0.6341 | 0.6341 | |
| 24 | Jeremy Stieler | 0 | 8 | 0.6341 | 0.6341 | |
| 26 | Heather Ellenberger | 0 | 8 | 0.6260 | 0.6260 | |
| 26 | Matthew Olguin | 0 | 8 | 0.6260 | 0.6260 | |
| 26 | Michael Moss | 0 | 8 | 0.6260 | 0.6260 | |
| 26 | Nicole Dike | 0 | 8 | 0.6260 | 0.6260 | |
| 26 | Paul Presti | 0 | 8 | 0.6260 | 0.6260 | |
| 26 | Randy Dick | 1 | 8 | 0.6260 | 0.6260 | |
| 32 | Antonio Mitchell | 0 | 8 | 0.6179 | 0.6179 | |
| 32 | Daniel Halse | 0 | 8 | 0.6179 | 0.6179 | |
| 32 | Keisha Vasquez | 1 | 8 | 0.6179 | 0.6179 | |
| 32 | Rafael Torres | 0 | 8 | 0.6179 | 0.6179 | |
| 32 | Ramar Williams | 0 | 8 | 0.6179 | 0.6179 | |
| 32 | Richard Beeghley | 1 | 8 | 0.6179 | 0.6179 | |
| 38 | Brayant Rivera | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Daniel Baller | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Darryle Sellers | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Derrick Elam | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Diance Durand | 1 | 8 | 0.6098 | 0.6098 | |
| 38 | George Brown | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Gregory Brown | 1 | 8 | 0.6098 | 0.6098 | |
| 38 | Jason Schattel | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Jeffrey Rudderforth | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Jonathon Leslein | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Kevin Buettner | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Nahir Shepard | 0 | 8 | 0.6098 | 0.6098 | |
| 38 | Vincent Scannelli | 0 | 8 | 0.6098 | 0.6098 | |
| 51 | Jared Kaanga | 0 | 8 | 0.6016 | 0.6016 | |
| 51 | Karen Richardson | 1 | 8 | 0.6016 | 0.6016 | |
| 51 | Montee Brown | 1 | 8 | 0.6016 | 0.6016 | |
| 51 | Pablo Burgosramos | 1 | 8 | 0.6016 | 0.6016 | |
| 51 | Stephen Bush | 1 | 8 | 0.6016 | 0.6016 | |
| 56 | Kristen White | 0 | 8 | 0.5935 | 0.5935 | |
| 56 | Michael Branson | 0 | 8 | 0.5935 | 0.5935 | |
| 58 | Randolph Tidd | 0 | 7 | 0.6697 | 0.5860 | |
| 59 | Jennifer Arty | 0 | 8 | 0.5854 | 0.5854 | |
| 59 | Kamar Morgan | 0 | 8 | 0.5854 | 0.5854 | |
| 59 | Melissa Printup | 0 | 8 | 0.5854 | 0.5854 | |
| 59 | Rachel Follo | 1 | 8 | 0.5854 | 0.5854 | |
| 59 | Scott Lefton | 0 | 8 | 0.5854 | 0.5854 | |
| 59 | Thomas Mccoy | 0 | 8 | 0.5854 | 0.5854 | |
| 59 | Walter Archambo | 0 | 8 | 0.5854 | 0.5854 | |
| 66 | David Humes | 0 | 8 | 0.5772 | 0.5772 | |
| 66 | Jeffrey Zornes | 0 | 8 | 0.5772 | 0.5772 | |
| 66 | Nicholas Nguyen | 0 | 8 | 0.5772 | 0.5772 | |
| 66 | Terry Hardison | 0 | 8 | 0.5772 | 0.5772 | |
| 70 | Nathan Brown | 0 | 7 | 0.6514 | 0.5700 | |
| 71 | Cade Martinez | 0 | 8 | 0.5691 | 0.5691 | |
| 71 | Edward Ford | 0 | 8 | 0.5691 | 0.5691 | |
| 71 | Louie Renew | 1 | 8 | 0.5691 | 0.5691 | |
| 71 | Ronald Schmidt | 0 | 8 | 0.5691 | 0.5691 | |
| 71 | Zechariah Ziebarth | 0 | 8 | 0.5691 | 0.5691 | |
| 76 | Anthony Brinson | 0 | 7 | 0.6449 | 0.5643 | |
| 77 | Trevor Macgavin | 0 | 8 | 0.5610 | 0.5610 | |
| 78 | Bryson Scott | 0 | 7 | 0.6355 | 0.5561 | |
| 79 | Philip Driskill | 1 | 7 | 0.6330 | 0.5539 | |
| 80 | Bunnaro Sun | 0 | 8 | 0.5528 | 0.5528 | |
| 81 | Patrick Tynan | 0 | 7 | 0.6296 | 0.5509 | |
| 82 | James Small | 0 | 7 | 0.6262 | 0.5479 | |
| 82 | Karen Coleman | 0 | 7 | 0.6262 | 0.5479 | |
| 84 | Jason Jackson | 0 | 8 | 0.5447 | 0.5447 | |
| 84 | Richard Conkle | 0 | 8 | 0.5447 | 0.5447 | |
| 86 | Kenneth Nielsen | 0 | 7 | 0.6168 | 0.5397 | |
| 87 | Ryan Baum | 0 | 7 | 0.6055 | 0.5298 | |
| 88 | Robert Lynch | 0 | 8 | 0.5203 | 0.5203 | |
| 89 | Daniel Major | 0 | 7 | 0.5926 | 0.5185 | |
| 90 | Earl Dixon | 0 | 7 | 0.5872 | 0.5138 | |
| 91 | Heather Kohler | 0 | 6 | 0.6848 | 0.5136 | |
| 92 | Akilah Gamble | 1 | 7 | 0.5794 | 0.5070 | |
| 92 | Darvin Graham | 0 | 7 | 0.5794 | 0.5070 | |
| 92 | Thomas Brenstuhl | 0 | 7 | 0.5794 | 0.5070 | |
| 95 | Brian Patterson | 0 | 7 | 0.5780 | 0.5057 | |
| 96 | Wayne Schofield | 0 | 8 | 0.5041 | 0.5041 | |
| 97 | Michael Linder | 0 | 6 | 0.6667 | 0.5000 | |
| 98 | Yiming Hu | 0 | 7 | 0.5701 | 0.4988 | |
| 99 | Gary Lawrence | 0 | 8 | 0.4959 | 0.4959 | |
| 100 | Nicholas Cinco | 0 | 6 | 0.6593 | 0.4945 | |
| 101 | Cheryl Brown | 0 | 7 | 0.5648 | 0.4942 | |
| 102 | Joseph Martin | 0 | 7 | 0.5607 | 0.4906 | |
| 102 | Marcus Evans | 0 | 7 | 0.5607 | 0.4906 | |
| 102 | Ryan Shipley | 0 | 7 | 0.5607 | 0.4906 | |
| 105 | Christopher Mulcahy | 0 | 7 | 0.5596 | 0.4896 | |
| 106 | Pamela Augustine | 0 | 6 | 0.6484 | 0.4863 | |
| 107 | Michael Moore | 0 | 7 | 0.5556 | 0.4861 | |
| 108 | Joshua Tracey | 0 | 7 | 0.5505 | 0.4817 | |
| 109 | Paul Seitz | 0 | 6 | 0.6374 | 0.4780 | |
| 110 | Kyle May | 0 | 7 | 0.5413 | 0.4736 | |
| 111 | Jordan Forwood | 0 | 6 | 0.6277 | 0.4708 | |
| 112 | Gabriel Quinones | 0 | 7 | 0.5321 | 0.4656 | |
| 113 | Clevante Granville | 0 | 6 | 0.6154 | 0.4615 | |
| 114 | Steven Webster | 0 | 7 | 0.5185 | 0.4537 | |
| 115 | Robert Sokol | 0 | 6 | 0.6044 | 0.4533 | |
| 116 | Amy Asberry | 0 | 6 | 0.6022 | 0.4516 | |
| 116 | Jonathan Knight | 0 | 6 | 0.6022 | 0.4516 | |
| 118 | Jay Kelly | 0 | 7 | 0.5140 | 0.4498 | |
| 119 | Jack Wheeler | 0 | 7 | 0.5138 | 0.4496 | |
| 120 | Brandon Parks | 0 | 6 | 0.5978 | 0.4484 | |
| 120 | Kevin Green | 0 | 6 | 0.5978 | 0.4484 | |
| 122 | Megan Fitzgerald | 0 | 6 | 0.5895 | 0.4421 | |
| 123 | Thomas Cho | 0 | 6 | 0.5870 | 0.4402 | |
| 124 | Jason Miranda | 0 | 6 | 0.5824 | 0.4368 | |
| 125 | Jennifer Wilson | 0 | 6 | 0.5806 | 0.4354 | |
| 125 | Noah Gosswiller | 0 | 6 | 0.5806 | 0.4354 | |
| 127 | Tara Bridgett | 0 | 6 | 0.5745 | 0.4309 | |
| 128 | Desmond Jenkins | 0 | 6 | 0.5604 | 0.4203 | |
| 128 | George Mancini | 0 | 6 | 0.5604 | 0.4203 | |
| 130 | Jose Torres Mendoza | 0 | 6 | 0.5579 | 0.4184 | |
| 131 | Jonathan Smith | 0 | 6 | 0.5484 | 0.4113 | |
| 132 | Jeremy Mounce | 0 | 5 | 0.6579 | 0.4112 | |
| 133 | Anthony Rockemore | 0 | 6 | 0.5376 | 0.4032 | |
| 134 | Brian Hollmann | 0 | 5 | 0.6267 | 0.3917 | |
| 135 | Jamie Ainsleigh-Wong | 0 | 6 | 0.5217 | 0.3913 | |
| 136 | Robert Martin | 0 | 6 | 0.5165 | 0.3874 | |
| 137 | David Hadley | 0 | 5 | 0.6104 | 0.3815 | |
| 138 | Steven Maisonneuve | 1 | 4 | 0.7458 | 0.3729 | |
| 139 | Andrew Gray | 0 | 6 | 0.4737 | 0.3553 | |
| 140 | Min Choi | 0 | 5 | 0.5526 | 0.3454 | |
| 141 | David Plate | 0 | 5 | 0.5513 | 0.3446 | |
| 142 | Sheryl Claiborne-Smith | 0 | 5 | 0.5455 | 0.3409 | |
| 143 | Wayne Gokey | 0 | 4 | 0.6190 | 0.3095 | |
| 144 | Ashlyn Dortch | 0 | 5 | 0.4805 | 0.3003 | |
| 145 | Cherylynn Vidal | 0 | 4 | 0.5938 | 0.2969 | |
| 146 | Steward Hogans | 0 | 4 | 0.5781 | 0.2890 | |
| 147 | Matthew Blair | 0 | 3 | 0.7333 | 0.2750 | |
| 148 | Lawrence Thuotte | 1 | 4 | 0.5484 | 0.2742 | |
| 149 | Vincent Kandian | 0 | 4 | 0.5000 | 0.2500 | |
| 150 | Donald Park | 0 | 3 | 0.5435 | 0.2038 | |
| 151 | Brittany Pillar | 0 | 2 | 0.7586 | 0.1897 | |
| 152 | Jeremy Krammes | 0 | 2 | 0.6875 | 0.1719 | |
| 153 | Ashley Johnson | 0 | 3 | 0.4565 | 0.1712 | |
| 154 | George Hall | 0 | 2 | 0.6250 | 0.1562 | |
| 155 | Adam Konkle | 0 | 2 | 0.5938 | 0.1484 | |
| 155 | Jeffrey Dusza | 0 | 2 | 0.5938 | 0.1484 | |
| 157 | David Kim | 0 | 2 | 0.5312 | 0.1328 | |
| 157 | Monte Henderson | 0 | 2 | 0.5312 | 0.1328 | |
| 159 | Gabrieal Feiling | 0 | 2 | 0.4688 | 0.1172 | |
| 160 | Clayton Grimes | 0 | 1 | 0.8750 | 0.1094 | |
| 161 | Tanaysa Henderson | 0 | 1 | 0.8571 | 0.1071 | |
| 162 | Brian Holder | 0 | 1 | 0.7500 | 0.0938 | |
| 162 | Sandra Carter | 0 | 1 | 0.7500 | 0.0938 | |
| 162 | Wallace Savage | 0 | 1 | 0.7500 | 0.0938 | |
| 165 | Daniel Gray | 0 | 1 | 0.6875 | 0.0859 | |
| 165 | Terrence Lee | 0 | 1 | 0.6875 | 0.0859 | |
| 167 | Antonio Chapa | 0 | 1 | 0.5000 | 0.0625 | |
| 167 | Zachary Brosemer | 0 | 1 | 0.5000 | 0.0625 | |
| 169 | Jasprin Smith | 0 | 1 | 0.3750 | 0.0469 | |
| 169 | Robert Epps | 0 | 1 | 0.3750 | 0.0469 | |
---
title: "2024 NFL Moneyline Picks"
output:
flexdashboard::flex_dashboard:
theme:
version: 4
bootswatch: spacelab
orientation: rows
vertical_layout: fill
social: ["menu"]
source_code: embed
navbar:
- { title: "Created by: Daniel Baller", icon: "fa-github", href: "https://github.com/danielpballer" }
---
```{r setup, include=FALSE}
# source_code: embed
library(flexdashboard)
library(tidyverse)
library(data.table)
library(formattable)
library(ggpubr)
library(ggrepel)
library(gt)
library(glue)
library(ggthemes)
library(hrbrthemes)
library(sparkline)
library(plotly)
library(htmlwidgets)
library(mdthemes)
library(ggtext)
library(ggnewscale)
library(DT)
source("./Functions/functions2.R")
thematic::thematic_rmd(font = "auto")
```
```{r Reading in our picks files, include=FALSE}
current_week = 8 #Set what week it is
week_1 = read_csv("./CSV_Data_Files/2024 NFL Week 1.csv") %>%
mutate(Name = str_to_title(Name))
week_2 = read_csv("./CSV_Data_Files/2024 NFL Week 2.csv")%>%
mutate(Name = str_to_title(Name))
week_3 = read_csv("./CSV_Data_Files/2024 NFL Week 3.csv")%>%
mutate(Name = str_to_title(Name))
week_4 = read_csv("./CSV_Data_Files/2024 NFL Week 4.csv")%>%
mutate(Name = str_to_title(Name))
week_5 = read_csv("./CSV_Data_Files/2024 NFL Week 5.csv")%>%
mutate(Name = str_to_title(Name))
week_6 = read_csv("./CSV_Data_Files/2024 NFL Week 6.csv")%>%
mutate(Name = str_to_title(Name))
week_7 = read_csv("./CSV_Data_Files/2024 NFL Week 7.csv")%>%
mutate(Name = str_to_title(Name))
week_8 = read_csv("./CSV_Data_Files/2024 NFL Week 8.csv")%>%
mutate(Name = str_to_title(Name))
# week_9 = read_csv("./CSV_Data_Files/2024 NFL Week 9.csv")%>%
# mutate(Name = str_to_title(Name))
# week_10 = read_csv("./CSV_Data_Files/2024 NFL Week 10.csv")%>%
# mutate(Name = str_to_title(Name))
# week_11 = read_csv("./CSV_Data_Files/2024 NFL Week 11.csv")%>%
# mutate(Name = str_to_title(Name))
# week_12 = read_csv("./CSV_Data_Files/2024 NFL Week 12.csv")%>%
# mutate(Name = str_to_title(Name))
# week_13 = read_csv("./CSV_Data_Files/2024 NFL Week 13.csv")%>%
# mutate(Name = str_to_title(Name))
# week_14 = read_csv("./CSV_Data_Files/2024 NFL Week 14.csv")%>%
# mutate(Name = str_to_title(Name))
# week_15 = read_csv("./CSV_Data_Files/2024 NFL Week 15.csv")%>%
# mutate(Name = str_to_title(Name))
# week_16 = read_csv("./CSV_Data_Files/2024 NFL Week 16.csv")%>%
# mutate(Name = str_to_title(Name))
# week_17 = read_csv("./CSV_Data_Files/2024 NFL Week 17.csv")%>%
# mutate(Name = str_to_title(Name))
# week_18 = read_csv("./CSV_Data_Files/2024 NFL Week 18.csv")%>%
# mutate(Name = str_to_title(Name))
# week_19 = read_csv("./CSV_Data_Files/2024 NFL Wild Card.csv")%>%
# mutate(Name = str_to_title(Name))
# week_20 = read_csv("./CSV_Data_Files/2024 NFL Divisional Round.csv")%>%
# mutate(Name = str_to_title(Name))
# week_21 = read_csv("./CSV_Data_Files/2024 NFL Conference Round.csv")%>%
# mutate(Name = str_to_title(Name))
# week_22 = read_csv("./CSV_Data_Files/2024 NFL Super Bowl.csv")%>%
# mutate(Name = str_to_title(Name))
#reading in scores
Scores = read_csv(glue::glue("./CSV_Data_Files/NFL_Scores_{current_week}.csv"))
#reading in CBS Prediction Records
cbs = read_csv(glue::glue("./CSV_Data_Files/CBS_Experts_{current_week}.csv")) %>%
mutate(Percent = round(Percent,4))
cbs_season = read_csv(glue::glue("./CSV_Data_Files/CBS_Experts_Season_{current_week}.csv"))
#reading in ESPN Prediction Records
espn = read_csv(glue::glue("./CSV_Data_Files/ESPN_Experts_{current_week}.csv"))%>%
mutate(Percent = round(Percent,4))
espn_season = read_csv(glue::glue("./CSV_Data_Files/ESPN_Experts_Season_{current_week}.csv"))%>%
mutate(Percent = round(Percent,4))
#Odds not working for the 2024 season. Need to fix scrape code for next year.
#Reading in the moneyline odds for each team and cleaning the team names
# odds_wk1 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_1.csv"))
# odds_wk2 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_2.csv"))
# odds_wk3 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_3.csv"))
# odds_wk4 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_4.csv"))
# odds_wk5 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_5.csv"))
# odds_wk6 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_6.csv"))
# odds_wk7 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_7.csv"))
# odds_wk8 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_8.csv"))
# odds_wk9 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_9.csv"))
# odds_wk10 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_10.csv"))
# odds_wk11 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_11.csv"))
# odds_wk12 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_12.csv"))
# odds_wk13 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_13.csv"))
# odds_wk14 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_14.csv"))
# odds_wk15 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_15.csv"))
# odds_wk16 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_16.csv"))
# odds_wk17 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_17.csv"))
# odds_wk18 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_18.csv"))
# odds_wk19 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_19.csv"))
# odds_wk20 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_20.csv"))
# odds_wk21 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_21.csv"))
# odds_wk22 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_22.csv"))
####################UPDATE THESE###############################
inst.picks = list(week_1, week_2, week_3, week_4, week_5, week_6, week_7, week_8) #, week_9, week_10, week_11, week_12, week_13, week_14, week_15, week_16, week_17 , week_18, week_19 , week_20, week_21, week_22) #add in the additional weeks
# odds = rbind(odds_wk1, odds_wk2, odds_wk3, odds_wk4, odds_wk5, odds_wk6, odds_wk7, odds_wk8,
# odds_wk9, odds_wk10, odds_wk11, odds_wk12) #add in the additional weeks
####################END OF UPDATE##############################
weeks = as.list(seq(1:current_week)) #creating a list of each week number
```
```{r read in scores clean data, include=FALSE}
#Cleaning Odds Data
# cl_odds = odds_cleaning(odds)
#Cleaning scores data
Scores = cleaning2(Scores)
#creating a list of winners for each week
winners = map(weeks, weekly_winners)
#creating a vector of this weeks winners
this_week = pull(winners[[length(winners)]])
#Getting the number of games for each week
weekly_number_of_games = map_dbl(weeks, week_number_games)
```
```{r Group Predictions, include=FALSE}
#Creating the list of everyones predictions each week.
games = map(inst.picks, games_fn)
#Creating the prediction table.
pred_table = map(games, pred_table_fn)
#Adding who won to the predictions
with_winners = map2(pred_table, winners, adding_winners)
#Creating results for each week.
results = map2(with_winners,weekly_number_of_games, results_fn)
```
```{r Displaying Group Results, echo=FALSE}
#Displaying the group results
inst_group_table = results[[length(results)]] %>% gt() %>%
cols_align(
align = "center") %>%
tab_header(
title = md("This Week's Predictions"),
#subtitle = md(glue("Week {length(results)}"))
) %>%
tab_style(
style = cell_text(color = "red", weight = "bold"),
locations = cells_body(
columns = c(Correct),
rows = Correct =="No"
)) %>%
tab_style(
style = cell_text(color = "green", weight = "bold"),
locations = cells_body(
columns = c(Correct),
rows = Correct =="Yes"
)) %>%
tab_options(
data_row.padding = px(3),
container.height = "100%"
)
```
```{r Weekly and season Group Results, include=FALSE}
# Printing the weekly and season win percentage
#how many games correct, incorrect, and not picked each week
weekly_group_correct = map(results, weekly_group_correct_fn)
#how many games were picked each week
weekly_games_picked = map2(weekly_group_correct, weekly_number_of_games, weekly_games_picked_fn)
#Calculating the number of correct picks for each week
weekly_group_correct_picks = map(weekly_group_correct, weekly_group_correct_picks_fn)
# Code to manually hard code in week where we get 0 games correct
# ##### Remove this line before next season
# weekly_group_correct_picks[[21]]=0
#Calculating weekly win percentage
weekly_win_percentage = map2(weekly_group_correct_picks, weekly_games_picked, weekly_win_percentage_fn)
#Calculating season win percentage
season_win_percentage = round(sum(unlist(weekly_group_correct_picks))/sum(unlist(weekly_games_picked)),4)
#Calculating number of games picked this season
season_games = sum(unlist(weekly_games_picked))
#calculating season wins
season_wins = sum(unlist(weekly_group_correct_picks))
#calculating the number of people who picked this week
Total = dim(inst.picks[[length(weeks)]])[1]
```
```{r plotting group results, include=FALSE}
#Previous Weeks
group_season_for_plotting = unlist(weekly_win_percentage) %>% as.data.frame() %>%
rename(`Win Percentage` = ".") %>%
add_column(Week = unlist(weeks))
```
```{r Plotting the group results, echo=FALSE}
inst_group_season_plot = group_season_for_plotting %>%
ggplot(aes(x = as.factor(Week), y = `Win Percentage`))+
geom_point()+
geom_path(aes(x = Week))+
ylim(c(0, 1)) +
xlab("NFL Week") +
ylab("Correct Percentage")+
ggtitle("Weekly Group Correct Percentage")+
theme_classic()+
theme(plot.title = element_text(hjust = 0.5, size = 18))
```
```{r beating cbs week, include=FALSE}
#Creating a list of correct percentages for each week.
cbs_weekly_percent = map(weeks, cbs_percent)
#Creating a list of how many cbs experts we beat each week.
cbs_experts_beat = map2(cbs_weekly_percent, weekly_win_percentage, experts_beat)
#Creating a list of how many cbs experts picked each week.
cbs_experts_total = map(cbs_weekly_percent, experts_tot)
```
```{r beating cbs season, include=FALSE}
#Creating a list of correct percentages for each week.
cbs_season_percent = map(weeks, cbs_season_percent)
#Creating a list of how many cbs experts we beat each week.
cbs_experts_beat_season = map2(cbs_season_percent, season_win_percentage, experts_beat)
#Creating a list of how many cbs experts picked each week.
cbs_experts_season_total = map(cbs_season_percent, experts_tot)
```
```{r beating ESPN week, include=FALSE}
#Creating a list of correct percentages for each week.
espn_weekly_percent = map(weeks, espn_percent)
#Creating a list of how many cbs experts we beat each week.
espn_experts_beat = map2(espn_weekly_percent, weekly_win_percentage, experts_beat)
#Creating a list of how many cbs experts picked each week.
espn_experts_total = map(espn_weekly_percent, experts_tot)
```
```{r beating ESPN season, include=FALSE}
#Creating a list of correct percentages for each week.
espn_season_percent = map(weeks, espn_season_percent)
#Creating a list of how many cbs experts we beat each week.
espn_experts_beat_season = map2(espn_season_percent, season_win_percentage, experts_beat)
#Creating a list of how many cbs experts picked each week.
espn_experts_season_total = map(espn_season_percent, experts_tot)
```
```{r individual results, include=FALSE}
#Creating a list of individual results for each week.
weekly_indiv = pmap(list(inst.picks, winners, weeks), indiv_weekly_pred)
#Combining each week into one dataframe and calculating percentage Correct for this week.
full_season = weekly_indiv %>% reduce(full_join, by = "Name") %>%
mutate(Percent = round(pull(.[,ncol(.)]/weekly_number_of_games[[length(weekly_number_of_games)]]),4))
#Creating a dataframe with only the weekly picks
a = full_season %>% select(starts_with("Week"))
#Creating a vector of how many weeks each person picked over the season
tot_week = NULL
help = NULL
for (i in 1:dim(a)[1]){
for(j in 1:length(a)){
help[j] = ifelse(is.na(a[i,j])==T,0,1)
tot_week[i] = sum(help)
}
}
#Creating a vector of how many games each person picked over the season
tot_picks= NULL
help = NULL
for (i in 1:dim(a)[1]){
for(j in 1:length(a)){
help[j] = unlist(weekly_games_picked)[j]*ifelse(is.na(a[i,j])==T,0,1)
tot_picks[i] = sum(help)
}
}
#Creatign a vector of how many games each person picked correct over the season
tot_correct = NULL
help = NULL
for (i in 1:dim(a)[1]){
tot_correct[i] = sum(a[i,], na.rm = T)
}
#adding how many weeks each person picked, season correct percentage, and adjusted season percentag to the data frame and sorting the data
indiv_disp = full_season %>% add_column(`Weeks Picked` = tot_week) %>%
add_column(tot_correct)%>%
add_column(tot_picks)%>%
mutate(`Season Percent` = round(tot_correct/tot_picks,4))%>%
mutate(`Adj Season Percent` = round(`Season Percent`*(tot_week/length(a)),4)) %>%
select(-tot_correct, -tot_picks) %>%
arrange(desc(Percent), desc(`Season Percent`)) %>%
mutate(Percent = ifelse(is.na(Percent)==T, 0, Percent))
```
```{r individual percentages, include=FALSE}
#Calculating individual percentages for each week.
weekly_indiv_percent = map2(weekly_indiv, as.list(weekly_number_of_games), indiv_percent) %>% reduce(full_join, by = "Name")
weekly_indiv_percent_plot = weekly_indiv_percent %>%
pivot_longer(cols = starts_with("Week"), names_to = "Week", values_to = "Percent")%>%
mutate(Percent = ifelse(is.na(Percent)==T, 0, Percent)) %>%
mutate(Week = as.factor(Week))
levels = NULL
for(i in 1:length(weeks)){
levels[i] = glue("Week {i}")
}
weekly_indiv_percent_plot = weekly_indiv_percent_plot %>%
mutate(Week = factor(Week, levels))
```
```{r sparklines, include=FALSE}
#adding sparklines
plot_group = function(name, df){
plot_object =
ggplot(data = df,
aes(x = as.factor(Week), y=Percent, group = 1))+
geom_path(size = 7)+
scale_y_continuous(limits = c(0,1))+
theme_void()+
theme(legend.position = "none")
return(plot_object)
}
sparklines =
weekly_indiv_percent_plot %>%
group_by(Name) %>%
nest() %>%
mutate(plot = map2(Name, data, plot_group)) %>%
select(-data)
indiv_disp_2 = indiv_disp %>%
inner_join(sparklines, by = "Name") %>%
mutate(`Season Trend` = NA)
```
```{r Printing Individual Table2, echo=FALSE}
# Printing the individual Table
indiv_table = indiv_disp_2 %>% gt() %>%
cols_align(
align = "center") %>%
tab_header(
title = md("Individual Results"),
subtitle = md(glue("Week {length(weeks)}"))
) %>%
tab_style(
style = cell_text(color = "red", weight = "bold"),
locations = cells_body(
columns = c(Percent),
rows = Percent<.5
)) %>%
tab_style(
style = cell_text(color = "green", weight = "bold"),
locations = cells_body(
columns = c(Percent),
rows = Percent>.5
)) %>%
tab_style(
style = cell_text(color = "red", weight = "bold"),
locations = cells_body(
columns = c(`Season Percent`),
rows = `Season Percent`<.5
)) %>%
tab_style(
style = cell_text(color = "green", weight = "bold"),
locations = cells_body(
columns = c(`Season Percent`),
rows = `Season Percent`>.5
))%>%
tab_style(
style = cell_text(color = "red", weight = "bold"),
locations = cells_body(
columns = c(`Adj Season Percent`),
rows = `Adj Season Percent`<.5
)) %>%
tab_style(
style = cell_text(color = "green", weight = "bold"),
locations = cells_body(
columns = c(`Adj Season Percent`),
rows = `Adj Season Percent`>.5
)) %>%
tab_options(
container.width = pct(100),
data_row.padding = px(1),
container.height = "100%"
) %>%
tab_spanner(
label = "Weekly # Correct",
columns = starts_with(c("Week "))
) %>%
text_transform(
locations = cells_body(c(`Season Trend`)),
fn = function(x){
map(indiv_disp_2$plot, ggplot_image, height = px(30), aspect_ratio = 4)
}) %>%
cols_hide(c(plot))
indiv_winners = indiv_disp_2 %>% filter(Percent == max(Percent)) %>% select(Name) %>% pull() %>% paste(collapse = ", ")
indiv_season = indiv_disp_2 %>% filter(`Season Percent` == max(`Season Percent`)) %>% select(Name) %>% pull() %>% paste(collapse = ", ")
indiv_season_adj = indiv_disp_2 %>% filter(`Adj Season Percent` == max(`Adj Season Percent`)) %>% select(Name) %>% pull()%>% paste(collapse = ", ")
```
```{r Printing Season Leaderboard, echo=FALSE}
# Printing the Season Leaderboard
season_leaderboard_disp = indiv_disp_2 %>% select(Name, starts_with("Week ")) %>%
pivot_longer(starts_with("Week"),names_to = "Week", values_to = "Correct") %>%
group_by(Week) %>%
mutate(Correct = case_when(is.na(Correct)==T~0,
TRUE~Correct)) %>%
mutate(Donut = case_when(Correct==max(Correct)~1,
TRUE~0)) %>%
ungroup() %>%
group_by(Name) %>%
summarise(`Donuts Won` = sum(Donut)) %>%
#mutate(`Donuts Won` = strrep("award,", Donuts)) %>%
right_join(.,indiv_disp_2) %>%
select(-starts_with("Week "), -Percent) %>%
mutate(`Season Rank` = min_rank(desc(`Season Percent`)),.before = Name) %>%
arrange(`Season Rank`)
season_leaderboard = season_leaderboard_disp %>%
gt() %>%
cols_align(
align = "center") %>%
tab_header(
title = md("Season Leaderboard (Season Percent)"),
subtitle = md(glue("Week {length(weeks)}"))
) %>%
# fmt_icon(
# columns = `Donuts Won`,
# fill_color = "gold",
# ) %>%
tab_style(
style = cell_text(color = "red", weight = "bold"),
locations = cells_body(
columns = c(`Season Percent`),
rows = `Season Percent`<.5
)) %>%
tab_style(
style = cell_text(color = "green", weight = "bold"),
locations = cells_body(
columns = c(`Season Percent`),
rows = `Season Percent`>.5
))%>%
tab_style(
style = cell_text(color = "red", weight = "bold"),
locations = cells_body(
columns = c(`Adj Season Percent`),
rows = `Adj Season Percent`<.5
)) %>%
tab_style(
style = cell_text(color = "green", weight = "bold"),
locations = cells_body(
columns = c(`Adj Season Percent`),
rows = `Adj Season Percent`>.5
)) %>%
tab_options(
container.width = pct(100),
data_row.padding = px(1),
container.height = "100%"
) %>%
tab_spanner(
label = "Weekly # Correct",
columns = starts_with(c("Week "))
) %>%
text_transform(
locations = cells_body(c(`Season Trend`)),
fn = function(x){
map(season_leaderboard_disp$plot, ggplot_image, height = px(30), aspect_ratio = 4)
}) %>%
cols_hide(columns = c(plot))
```
```{r Printing Adj Season Leaderboard, echo=FALSE}
# Printing the Adj Season Leaderboard
adj_season_leaderboard_disp = indiv_disp_2 %>% select(Name, starts_with("Week ")) %>%
pivot_longer(starts_with("Week"),names_to = "Week", values_to = "Correct") %>%
group_by(Week) %>%
mutate(Correct = case_when(is.na(Correct)==T~0,
TRUE~Correct)) %>%
mutate(Donut = case_when(Correct==max(Correct)~1,
TRUE~0)) %>%
ungroup() %>%
group_by(Name) %>%
summarise(`Donuts Won` = sum(Donut)) %>%
#mutate(`Donuts Won` = strrep("award,", Donuts)) %>%
right_join(.,indiv_disp_2) %>%
select(-starts_with("Week "), -Percent) %>%
mutate(`Season Rank` = min_rank(desc(`Adj Season Percent`)),.before = Name) %>%
arrange(`Season Rank`)
adj_season_leaderboard = adj_season_leaderboard_disp %>%
gt() %>%
cols_align(
align = "center") %>%
tab_header(
title = md("Season Leaderboard (Adjusted Season Percent)"),
subtitle = md(glue("Week {length(weeks)}"))
) %>%
# fmt_icon(
# columns = `Donuts Won`,
# fill_color = "gold",
# ) %>%
tab_style(
style = cell_text(color = "red", weight = "bold"),
locations = cells_body(
columns = c(`Season Percent`),
rows = `Season Percent`<.5
)) %>%
tab_style(
style = cell_text(color = "green", weight = "bold"),
locations = cells_body(
columns = c(`Season Percent`),
rows = `Season Percent`>.5
))%>%
tab_style(
style = cell_text(color = "red", weight = "bold"),
locations = cells_body(
columns = c(`Adj Season Percent`),
rows = `Adj Season Percent`<.5
)) %>%
tab_style(
style = cell_text(color = "green", weight = "bold"),
locations = cells_body(
columns = c(`Adj Season Percent`),
rows = `Adj Season Percent`>.5
)) %>%
tab_options(
container.width = pct(100),
data_row.padding = px(1),
container.height = "100%"
) %>%
tab_spanner(
label = "Weekly # Correct",
columns = starts_with(c("Week "))
) %>%
text_transform(
locations = cells_body(c(`Season Trend`)),
fn = function(x){
map(adj_season_leaderboard_disp$plot, ggplot_image, height = px(30), aspect_ratio = 4)
}) %>%
cols_hide(columns = c(plot))
```
```{r instructor formattable, echo=FALSE}
improvement_formatter <-
formatter("span",
style = x ~ formattable::style(
font.weight = "bold",
color = ifelse(x > .5, "green", ifelse(x < .5, "red", "black"))),
x ~ icontext(ifelse(x == max(x), "star", ""), x))
indiv_disp_3 = indiv_disp_2 %>% select(-plot)
indiv_disp_3$`Season Trend` = apply(indiv_disp_3[,2:(1+length(weeks))], 1, FUN = function(x) as.character(htmltools::as.tags(sparkline(as.numeric(x), type = "line", chartRangeMin = 0, chartRangeMax = 1, fillColor = "white"))))
indiv_table_2 = as.htmlwidget(formattable(indiv_disp_3,
align = c("l", rep("c", NROW(indiv_disp_3)-1)),
list(`Season Percent` = color_bar("#FA614B"),
`Season Percent`= improvement_formatter,
`Adj Season Percent`= improvement_formatter)))
indiv_table_2$dependencies = c(indiv_table_2$dependencies, htmlwidgets:::widget_dependencies("sparkline", "sparkline"))
```
```{r Plotting individual results over the season2, eval=FALSE, include=FALSE, out.width="100%"}
#Creating the individual plot.
inst_indiv_plots = weekly_indiv_percent_plot %>%
ggplot(aes(x = factor(Week), y = Percent, color = Name))+
geom_point()+
geom_path(aes(x = as.factor(Week), y = Percent, color = Name,
group = Name))+
ylim(c(0, 1)) +
labs(x = "NFL Week",
y = "Correct Percentage",
title = "Weekly Individual Correct Percentage")+
facet_wrap(~Name)+
theme_classic()+
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5, size = 18),
axis.text.x=element_text(angle =45, vjust = 1, hjust = 1))
```
```{r data for data page}
inst.data = map2(inst.picks, weeks, disp_data) %>% bind_rows()
```
```{r fivethirtyeight}
inst_538 = map(results, five38) %>% unlist() %>% sum()
```
```{r pregame, eval=FALSE, include=FALSE}
#Predictions for the week
#Creating the list of group predictions each week.
games = map(inst.picks, games_fn)
#Creating the prediction table.
pred_table = map(games, pred_table_fn)
#Printing table of instructor predictions
pred_table[[length(pred_table)]] %>% mutate(Game = row_number()) %>%
rename(`Votes For` = votes_for, `Votes Against` = votes_against) %>%
gt() %>%
cols_align(
align = "center") %>%
tab_header(
title = md("This Week's Predictions"),
subtitle = md(glue("Week {length(weeks)}"))
) %>%
tab_options(
data_row.padding = px(3)
)
```
Group Predictions
==========================================================================
Sidebar {.sidebar}
-------------------------------------
#### CBS Sports
<font size="4">
This week we beat or tied `r cbs_experts_beat[[length(weeks)]]` of `r cbs_experts_total[[length(weeks)]]` CBS Sports' Experts.
For the season we are currently beating or tied with `r cbs_experts_beat_season[[length(weeks)]]` of `r cbs_experts_season_total[[length(weeks)]]` CBS Sports' Experts.
</font>
#### ESPN
<font size="4">
We also beat or tied `r espn_experts_beat[[length(weeks)]]` of `r espn_experts_total[[length(weeks)]]` ESPN Experts.
For the season we are currently beating or tied with `r espn_experts_beat_season[[length(weeks)]]` of `r espn_experts_season_total[[length(weeks)]]` ESPN Experts.
</font>
Row
--------------------------------------
### Win percentage for the week
```{r}
inst_rate <- weekly_win_percentage[[length(weekly_win_percentage)]]*100
gauge(inst_rate, min = 0, max = 100, symbol = '%', gaugeSectors(
success = c(55, 100), warning = c(40, 54), danger = c(0, 39)
))
```
### Season Win Percentage
```{r}
inst_season <- season_win_percentage*100
gauge(inst_season, min = 0, max = 100, symbol = '%', gaugeSectors(
success = c(55, 100), warning = c(40, 54), danger = c(0, 39)
))
```
### Games Correct
```{r}
valueBox(value = season_wins,icon = "fa-trophy",caption = "Correct Games this Season")
```
### Games Picked
```{r}
valueBox(value = season_games,icon = "fa-clipboard-list",caption = "Games Picked this Season")
```
### Number of predictions
```{r}
valueBox(value = Total,icon = "fa-users",caption = "Predictions this week")
```
Row
--------------------------------------
###
```{r}
inst_group_table
```
###
```{r}
ggplotly(inst_group_season_plot) %>%
layout(title = list(y = .93, xref = "plot"),
margin = list(t = 40))
```
Individual Predictions
==========================================================================
Sidebar {.sidebar}
-------------------------------------
#### Best Picks of the Week.
<font size="4">
`r indiv_winners`
</font>
#### Best Season Correct Percentage
<font size="4">
`r indiv_season`
</font>
#### Best Adjusted Season Correct Percentage
<font size="4">
`r indiv_season_adj`
* Adjusted season percentage accounts for the number of weeks picked.
</font>
row {.tabset}
--------------------------------------
### Individual Table
```{r}
indiv_table
```
<!--
### Individual Table2
```{r, out.height="100%"}
indiv_table_2
```
-->
<!--
### Individual Plots
```{r, out.width="100%"}
#ggplotly(inst_indiv_plots)
```
-->
### Season Leaderboard
```{r, out.width="100%"}
season_leaderboard
```
### Adjusted Season Leaderboard
```{r, out.width="100%"}
adj_season_leaderboard
```
Data
==========================================================================
```{r}
datatable(
inst.data, extensions = 'Buttons', options = list(
dom = 'Blfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
lengthMenue = list( c(10, 25, 50, 100, -1), c(10, 25, 50, 100, "All") )
)
)
```